You are evaluating a classification model and notice that the error on the test set is significantly greater than on the training set. You determine that the model is overfitting the training data. What could you do to prevent overfitting? Reduce the number of features to model O Increase the train/test split ratio O Decrease the train/test split ratio O Uuse a different error function

Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
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You are evaluating a classification model and notice that the error on the test set is significantly
greater than on the training set. You determine that the model is overfitting the training data. What
could you do to prevent overfitting?
Reduce the number of features to model
Increase the train/test split ratio
Decrease the train/test split ratio
Use a different error function
NEXT
Transcribed Image Text:5 of 5 You are evaluating a classification model and notice that the error on the test set is significantly greater than on the training set. You determine that the model is overfitting the training data. What could you do to prevent overfitting? Reduce the number of features to model Increase the train/test split ratio Decrease the train/test split ratio Use a different error function NEXT
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